Climate and environmental change drives Ixodes ricinus
geographical expansion at the northern range margin
Additional file 1
S. Jore1*, S.O. Vanwambeke2, H. Viljugrein1,3, K. Isaksen4, A.B. Kristoffersen1,5, Z.
Woldehiwet6, B. Johansen7, E. Brun1, H. Brun-Hansen8, S. Westermann9, I.L. Larsen1, B.
Ytrehus1 and M. Hofshagen1
1
Norwegian Veterinary Institute, Ullevålsveien 68, P.O.Box 750 Sentrum 0106 Oslo, Norway
2
Georges Lemaître Centre for Earth and Climate Research, Earth & Life Institute, Université
Catholique de Louvain, Place Louis Pasteur 3, B1348 Louvain-la-Neuve, Belgium
3
Centre for ecological and evolutionary synthesis (CEES), Department of Biology, University
of Oslo, P.O.Box 1047, Blindern, 0316 Oslo, Norway
4
The Norwegian Meteorological Institute, Research and Development Department, Division
for Model and Climate Analysis, P.O.Box 43 Blindern, 0313 Oslo, Norway,
5
Department of Informatics, University of Oslo, P.O.Box 1080, Blindern, 0316 Oslo, Norway
6
Department of Infection Biology, Institute of Infection & Global Health, University of
Liverpool, Leahurst Campus, Chester High Road, Neston, Wirral CH64 7TE, United
Kingdom
7
Northern Research Institute, P.o.b 6434 Forskningsparken, 9294 Tromsø, Norway
8
Norwegian School of Veterinary Science, Ullevålsveien 72, P.O.Box 8146 Dep., 0033 Oslo,
Norway
9
Department of Geosciences, University of Oslo, , P.O.Box 1066, Blindern, 0316 Oslo,
*Corresponding author:
Solveig.jore@vetinst.no
1
Contents
Additional file 1 Introduction ..................................................................................................... 3
Additional file 1 Methods .......................................................................................................... 3
Additional file 1 Discussion ..................................................................................................... 12
Additional file 1 References ..................................................................................................... 27
2
Additional file 1 Introduction
In this supplementary material we present data and methodology in more detail.
Additional file 1 Methods
Description of the study area
The sampling frame consisted of three geographical areas: five interior municipalities of the
county of Agder and Telemark (INLAND): Kviteseid, Fyresdal, Tokke, Vinje og Bygland,
seven municipalities belonging to the county of Rogaland (COAST): Sandnes, Lund,
Bjerkreim, Hå, Time, Gjesdal and Kvitsøy and two municipalities in of Ryfylke, northern part
of Rogaland county and Hordaland county (FJORD): Vindafjord and Etne. According to
published tick distribution maps [1-3] we would not expect to have ticks present in the 80’s in
the selected municipalities of Agder and Telemark, but expect to see some changes during the
decades. The treeless and wind-swept Jæren, was according to Tambs-Lyche, free from ticks.
Lastly, in the county of Rogaland ticks have been present for a long time, but changes in
seroprevalence might have occurred through the decades. Samples were selected from the
three different regions from the 80s, the 90s and from year 2000 and onwards.
The elevation of farms in INLAND (mean 377 meters above sea level (masl)), was higher
than those of the COAST (mean 109 masl) and FJORD (mean 57 masl). Two types of grazing
systems were encountered: infield grazing in fenced pastures near or around the farm and
rough grazing in semi-natural forest/ mountain pastures away from the farm during the
summer and autumn. On average infield pastures were located at 169 masl (range 10 – 748
masl) and the rough grazing pastures varied between lower bounds (range: 136-1400 masl and
mean: 448.7 masl) and at higher bounds (range: 173.8-1530 masl and mean: 658.6 masl).
INLAND is characterized by deep valleys with small patches of agricultural land along rivers
and deep, long inland fjords. Many valley sides are covered by dense spruce forest, with pine
3
at higher altitudes, and also large areas of bare rock. Hills and mountains separating the
valleys are covered with forests with scattered pine and birch trees and large areas of peat
bogs. Mountain tops are low and rounded with thin soils only partly covered with grass,
lichens, mountain birch and crowberries.
The outer, western part of COAST consists of a relatively flat agricultural landscape
dominated by fields, meadows and cultivated pastures divided by old stone walls. There are
relatively few large trees, though small hedges and plantations of coniferous trees appeared
over the last century. The eastern part of the district is a hillier landscape consisting of small
valleys with creeks and lakes surrounded by agricultural land. The valleys are interrupted by
rocks, cliffs and outcrops almost without soils. The vegetation is low, mostly dominated by
heather, but in sheltered spots small forests of birch and oak may grow.
FJORD consists of a rugged, but lush and moist landscape of richly branching fjords and
valleys. The valley bottoms often have nutritious soils and are characterized by small fields,
meadows and pastures divided by temperate broadleaf forests with oak, maples, birches and
other deciduous trees. At higher altitudes, forests are dominated by pine, with occasional
spruce plantations. The valleys and fjords are divided by small, rugged and rounded
mountains with much bare rock, only partly covered with blueberries and mountain grasses
The INLAND municipalities are partially lee areas in relation to the large weather systems
mainly coming from the west. However the westerly municipalities receive more precipitation
than the easterly area. There is also a gradient from the coast to the inland, which results in
the driest areas in the north-easterly parts of the municipalities. In spring the mountainous
areas in the north of Aust-Agder and Telemark have a mean air temperature between −6 and
−2 °C. During the summer the warmest areas are in the south-east with mean air temperature
of 14-16 °C. The areas in the west are under the influence of the North Sea and have lower
summer mean air temperature, i.e between 12 and 14 °C. The lowest mean air summer
4
temperature is found in the mountainous areas with around 6 °C at the highest elevations. The
region can during the summer months experience considerable precipitation with
thunderstorms which often is connected with periods of high air temperatures. During the
autumn and winter the distance to coast is most important for determine the air temperature.
Along the coast the mean air temperature is above 8 °C while mean temperature in
mountainous areas is close to 0 °C. During winter the mean air temperature along the coast is
between 0 and 2 °C, while negative mean air temperatures dominate the rest of the region.
COAST: There are two primary drivers of the weather – the sea and the high mountains
further into the countryside. This leads to a mild and humid climate. The frontal precipitation
dominates, and most of the precipitation is received during autumn and winter. The driest
areas (e.g. Kvitsøy) have an annual precipitation of about 1100 mm. The wettest areas have
more than 2000 mm. In the springtime the mean air temperature is around 4-6 °C and during
the summer 13-15 °C and the area also receive a lot of precipitation. During the autumn Jæren
belongs to the hottest areas on the west coast, the mean air temperature is around 8 °C. During
the wintertime the mean air temperature is 0 °C and the precipitation is primarily consisting of
rain. FJORD: this area has many mutual weather characteristics with Jæren. However due to
the nearby mountains both frontal and orographic precipitation dominates, thus receive more
snow and considerably more precipitation compared to the outermost areas of Jæren.
Description of the development of the cervid population in the study area
The most prevalent cervid in INLAND is the moose. Since 1980, the number of hunted moose
showed a steady increase until around 1999 when there was a moderate decline in the growth
(0.2 shot moose/ km2) followed by a moderate decline starting in 2005. Red deer was virtually
absent from INLAND in the 1980s and only few animals were shot in the 1990s, but from the
start of the 2000s this species became more prevalent in the area with 147 animals shot in
2007 (0.04/ km2). The roe deer population in this relatively snow-covered and cold area is
5
typically relatively small and characterized by rapid fluctuations largely influenced by the
winter conditions.
Back in the 1980s the most prevalent cervid in COAST used to be roe deer. The population in
this area showed a steady increase through the study period, from 0.025 animals shot per
square kilometer in 1980 to 0.20 in 2008. However, while being absent from the area in the
early 1980s, the red deer population of COAST started to increase from the early 1990s,
equaling the roe deer hunting bag in 2005 and reaching 0.22 animals/km2 in 2007. The moose
is only rarely seen in the western part of COAST, but there are persisting populations in the
mountain pasture municipalities. The population has showed the same trends as the moose
population in INLAND, with an increase up to the end of the 1990s followed by a moderate
decline, but at a much lower density (0.04 animals/km2).
FJORD has been known as a red deer area for a long period of time. However, also in this
area there has been a pronounced increase in hunting bags through the study period,
increasing from 0.04 animals shot per km2 in 1980 to 0.27 in 2007. In the 1980s roe deer was
absent from the area, but has become more prevalent and in 2008 0.05 animals were shot per
sqkm. Moose are rare in this area.
Overall, each district is dominated by a different cervid but all three areas have experienced a
marked increase in abundance of cervids. However, the overall cervid density seems to vary
between the areas, with INLAND having a much lower average density of cervids (0.2 shot
cervids/ km2) compared to COAST and FJORD (0.38 and 0.28/ km2 respectively in 2005).
Collection of samples and sample size
The serum samples belong to NVI’s sample and culture collection. In the late 70s, 80s and
90s diagnostic samples were taken from sheep farms or contact farms to investigate
serological evidence of infection with maedi-visna virus, Border disease virus or Louping ill
virus (descriptive study; unpublished, J. Krogsrud). In 1997 a control programme was
6
launched for meadi-visna in all flocks in high-risk regions (Rogaland and Hordaland counties)
which lasted for seven years. From 2003 a nationwide surveillance and control program for
maedi-visna was established by randomly selecting flocks of participating in ram circles were
and testing all flocks belonging to the same ram circle. The collection of serum samples was
carried out by official veterinarians. We aimed to get at least 300 samples from each decade
for the chosen geographical area. The samples included were collected throughout the year.
Negative controls for the laboratory analyses were sampled and tested in June 2010 from a
farm located between 70-71°N.
ELISA
An enzyme-linked immunosorbent assay (ELISA) was used to test for the presence of
antibodies against A. phagocytophilum in sheep, [4,5]with minor modifications.
Purified preparations of bacteria grown in tick cells, treated with 0.1% P-40 (Sigma), were
used to coat ELISA microtitre plates by overnight incubation at 4oC in carbonate bicarbonate
buffer (pH 9.6), 96-well flat-bottomed plates (MaiSorp Immunoplates, Nunc, Denmark) were
coated with 50 μl of antigen diluted in carbonate bicarbonate buffer, pH 9.6, to give a final
concentration of 20 μg/ml; the plates were then incubated overnight at 4ºC. The remaining
binding sites were then blocked by adding 100 μl of 0.2% bovine serum albumin (BSA) in
sample diluent (0.5M Tris-HCl, pH 7.4 and 1mM EDTA) and incubation for an hour at 37°C.
The plates were washed five times with wash buffer. The test and standard positive and
negative sera were diluted 1:200 in sample diluent buffer and 50 μl of each dilution added to
triplicate wells. After incubation overnight at 4ºC, the plates were washed five times before
adding 50 μl of an optimal dilution (in sample diluent) of monoclonal mouse anti-sheep IgG
horseradish peroxidase (HRP, Sigma Aldrich, Poole, Dorset, UK). After incubation at 37ºC for
1 h, the plates were washed 5 times. Then 100 μl of freshly prepared soluble substrate for
HRP (o-phenylenediamine dihydrochloride, as SIGMAFAST OPD, Sigma Aldrich, Poole,
Dorset, UK), with optimal concentrations of fresh H2O2 were added to each well. The plates
7
were left at room temperature in the dark for 20 min for colour development before stopping
the reaction by adding 50 μl of 2.5N H2SO4. The optical density (OD) of each well was then
determined using a micro-plate reader (MRX Microplate Reader, Dynex Technologies,
Worthing, West Sussex, UK) with a test wavelength of 490 nm. Each test run included a
positive reference serum and a negative control serum. The absorbance value of each test
sample was then expressed as a ratio of positivity (PP) using the formula:
𝑂𝐷𝑡𝑒𝑠𝑡−𝑂𝐷𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑐𝑜𝑛𝑡𝑟𝑜𝑙
PP = 𝑂𝐷𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑐𝑜𝑛𝑡𝑟𝑜𝑙−𝑂𝐷𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑐𝑜𝑛𝑡𝑟𝑜𝑙
OD: optical density
Positive control: serum from sheep experimentally infected with A. phagocytophilum and
shown to have antibodies against A. phagocytophilum.
Negative control: serum from sheep bred and raised in tick-free environment and known to be
negative for antibodies against A. phagocytophilum
The cut-off point between positive and negative samples was 0.20 PP. This was based on the
mean PP value + 2 standard deviations of several negative ovine sera [5].
Climatic data
Air temperature and precipitation obtained from daily observations were interpolated to a 1x1
km2 grid covering the Norwegian mainland [6-8]. Daily grids have been made available since
1957 and can be accessed at www.seNorge.no. Air temperature was estimated from a residual
kriging approach using terrain and geographic position to describe the deterministic
component [9]. Precipitation was interpolated using triangular irregular networks (TINs). A
terrain adjustment was performed on the precipitation grid, according to the assumption that
8
precipitation increases by 10% per 100 m up to 1000 masl and 5% above that [10,11].
Precipitation and temperature grids are input in a precipitation/degree-day snow model with a
snow routine similar to the HBV hydrology model (Hydrologiska Byråns
Vattenbalansavdelning modell; Bergström, 1992) as described previously [12]. Temperature
dependent thresholds were used to separate snow from rain (T = 0.5 °C) and to determine
snow melt and refreezing (T = 0.0 °C). Snow depth was estimated from the amount of
existing snow and fresh snow reduced by melting and compaction [13].
Relative humidity was obtained from a new set of a high-resolution hindcast data produced at
The Norwegian Meteorological Institute [14,15]. It was produced using a hydrostatic
numerical weather prediction model, the High Resolution Limited Area Model (HIRLAM)
[16] with 10 km horizontal resolution and 40 vertical layers. The boundary values were taken
from a global reanalysis project, ERA40, at the European Centre for Medium Range Weather
Forecasts (ECMWF). After August 2002 the boundary values are from the operational
weather forecasting model at ECMWF.
Ground surface temperatures were calculated using a soil thermal model, which numerically
solves Fourier’s Law of heat transfer in the ground and snow cover [17]. The model accounts
for latent heat effects during soil freezing and thawing and allows for a dynamic upper
boundary, so that the build-up and ablation of the seasonal snow cover can be included [17].
The modeled soil domain extends to a depth of 100m, with a grid spacing increasing from
0.05m in the snow cover and in the close to the soil surface to 10m at the lower boundary. For
each of the grid cells, the volumetric fractions of air, mineral and water are prescribed, with
water changing to ice according to a freezing characteristic following Dall’Amico et al. 2011
[18]. Hereby the sum of water and ice contents remains constant in time. The thermal
properties of each grid cell were calculated from the volumetric fractions of the soil
constituents [17]. The model is driven by gridded data of air temperature and snow depth
from the gridded data set described above. At the lower boundary a constant geothermal heat
9
flux is prescribed. The soil domain is initialized to steady-state conditions of the first five
years in the gridded data set and the soil thermal model is subsequently run at daily
resolution. As ground surface temperature, we employ the temperature of the uppermost cell
of the soil domain extending from the soil surface to a depth of 0.05m.
Remote sensing data from Landsat 5
Material
Landsat images were retrieved for the summer of 2006 (Landsat 5TM) and the summer of
1984/1988 (Landsat 5TM). All the Landsat TM/ETM+ data sets collected were georeferenced to the UTM map format, zone 32, WGS84, using the control-point correction
method with a root-mean square error of less than one pixel. The NDVI image processing
was performed using ENVI image processing software. The geographic information analyses
were performed on Arc Gis - Geographical Information system.
Various mathematical combinations of spectral channels have been applied as sensitive
indicators of the presence and condition of green vegetation [19]. Most simple of the
vegetation indices is the vegetation index (VI), defined as "the ratio between the near-infrared
channel and the red channel". This equation has further been developed into the Normalised
Difference Vegetation Index (NDVI). NDVI was found [19-21] to be a representative of
plant assimilation condition and of its photosynthetic efficiency. NDVI is an indicator of the
density of chlorophyll and leaf tissue calculated from the red and near infrared bands:
NDVI = (NIR-RED) / (NIR+RED)
In this equation NIR represents the Near Infrared band 4 (0.76-0.90 µm) of Landsat 5 and 7
and RED the corresponding band 3 (0.63-0.69 µm). NDVI gives values between -1 and + 1.
Vegetated areas in general yield high values for these indices due to their high near infrared
10
reflectance and low visible reflectance. Reflectance of cloud, snow and water is larger in the
red than near infrared. Clouds and snowfields yield negative values while water has very low
or slightly negative values. Rock and bare soil have approximately similar reflectance values
in the red and near infrared channels, and results in indices near zero. A zero or close to zero
means no vegetation. [22,23]. The NDVI is further used for deducing temporal changes in the
vegetation cover. Temporal changes in NDVI are related to the seasonal changes in the
amount of photosynthetic tissues; typically NDVI increases in spring, saturates at a certain
point of greenness in summer and then declines in autumn, at mid to high latitudes. The
NDVI equation has a simple, open loop structure. This renders the NDVI susceptible to large
sources of error and uncertainty over variable atmospheric and soil background conditions,
wetness, imaging geometry, and with changes within the canopy itself [21,22].
Methods
All images were processed into NDVI and mosaicked by time period. Images from the two
time periods were then differenced to obtain the change in NDVI. Based on visual
interpretation of aerial pictures from “Norway in pictures” (http://www.norgeibilder.no/), the
NDVI difference image was sliced into 1) areas of significant change and 2) others. The
binary map showing these two categories was overlaid with the CORINE map. Pixels that had
important changes in NDVI, and which were classified as forest or semi-natural areas (broadleaved forest, coniferous forest, mixed forest and transitional woodland shrub) in the 2006
CORINE map, were extracted for further analyses. These pixels were interpreted to constitute
areas of bush encroachment, i.e. areas that in the last, but not the first time period had low
woody vegetation. This resulted in a binary map of bush encroachment. Buffer zones of a
500-m radius were established around farms and pasture areas. The total area of bush
encroachment within each zone was calculated. The structure of the bush encroachment was
characterized by the number of patches found within each zone, and their mean area. No data
11
was calculated for zones found in cloudy areas of the image, as per visual examination of the
original images.
Additional file 1 Discussion
Results for multivariable regression
The AIC for the null-model with outcome and only random effects (Timespan and
municipality), was 3293, whilst AIC for the best model was 2748. The residuals of the final
model showed no remaining pattern (for spatial pattern, see Additional file 1 Figure S6). All
pairwise correlations between explanatory variables included and those not included in the
final model were equal or less than 0.7, except for timespan with roe deer (0.73), red deer
with district (0.76) and snow start with mean ambient temperature (0.74).
Distribution of Ixodes ricinus
Even though I. ricinus is the only known vector of A. phagocytophilum in Northern Europe,
other tick species could in theory transmit this pathogen. Of the few tick species present in
Norway [1], I. trianguliceps would be the most plausible candidate. However, as it is very
unlikely that any of the stages of this tick species feed on large mammals as sheep [24] this
can be excluded. A cross-reaction between A. marginale and A. phagocytophilum antibodies
has been reported [25]. However, as far as we know A. marginale infection has never been
diagnosed in Norway.
Climatic factors
Annual air temperatures (Figure 1 panel A) have increased by 0.8-1.3 °C in all three districts,
with INLAND having the greatest increase. Annual precipitation trends were not so clear due
to several wet years in the early 80s (Figure 1 panel B). However, a positive increase of 5-10 %
was recorded in several COAST and westernmost FJORD municipalities and southern and
northern INLAND municipalities. In some of the easternmost FJORD and COAST and
12
central INLAND municipalities precipitation trends were slightly negative. Trends in annual
maximum snow depth (Figure 1 panel C) were generally negative in all three districts, except
in the highest mountain and inland areas. This is in line with recent warming (cf. Figure 1
panel A), as more and more precipitation falls as rain and melting increases in lowland areas
while maximum snow depth may increase during sufficiently cold winters [26]. The number
of near-zero events (Figure 1 panel D, defined as days having daily mean temperature
between −1.5 and 1.0 °C) had increased with time in INLAND and in the eastern pasture
municipalities, while there were no clear trends in the COAST and INLAND farm
municipalities. A and B are consistent with previous studies [27]. The results of the trend
analysis are highly sensitive to the analysed time period and its length, due to the strong
natural variability in Norway [26].
Uncertainties associated with the climate data mainly relate to the gridding procedure. The set
of measurements used in the interpolation varies from day to day depending on availability
and quality, which might have influenced temporal trends. According to Tveito et al. [8],
temperature grids perform well except in cases of temperature inversion during winter time.
Cross validation showed that the temperature grids generally perform better during the
summer and that errors increase with decreasing temperatures. Precipitation is a more
challenging element to interpolate because of its complex nature. Thus precipitation grids
deviate more from reality. Despite the above-mentioned uncertainties we consider the data to
be sufficiently accurate for the purpose of regional scale analysis.
13
Figure S1: Trends in relevant climate variables for period 1981-2010. Annual air
temperature (A), Annual precipitation (B), Annual maximum snow depth (C) and Number of
near-zero events (D). neg.: negative; pos.: positive; sign.: significant; notsign.: not significant.
Statistical significance is at the 5% error level (C and D generated from Dyrrdal et al. [26]).
Figure S2: Abundance of Moose, red deer and roe deer during timespan 1 (1980’s) as
14
Figure S2: Abundance of Moose, red deer and roe deer during timespan 1 (1980’s) as represented by hunting
bags recorded in the different districts during that period.
15
Figure S3: Abundance of moose, red deer and roe deer during timespan 2 (1990’s) as
represented by hunting bags recorded in the differenct districts during that period.
Figure S4: Abundance of moose, red deer and roe deer during timespan 3 (2000’s) as
represented by hunting bags recorded in the differenct districts during that period.
16
Figure S5: Estimated prevalence of seropositive and seronegative sheep in all districts using
the final model.
Figure S6: Spatial plot of residuals after removing five outliers from the final model.
17
Table S1: Prevalence of Anaplasma phagocytophilum in farms in the three different
geographical areas test during timespan 1 (1978 -1989), timespan 2 (1990 –1999) and
timespan 3 (2000- 2008). *Four farms were sampled more than once; two of these were
sampled during two different timespans.** This farm is one of the farms in Norway located at
the highest altitudes. The farmer had not bought any ewes from other flocks but has used rams
from nearby farms. Both the farmer and the agricultural office in the municipality reported
that farmers have found ticks on their dogs which had frequented the same rough grazing area
as used by the sheep in the farm, and in infields of other farms located at similar elevations
(~850 masl); one tick from a dog was identified as I. ricinus.
District
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
INLAND
COAST
COAST
COAST
Timespan Farm
id
1
1
1
1
1
1
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
1
1
1
20
8*
6
9
16
5*
4
21
18
19
15
12
13
7
10
11
5*
14
2
3
1
17
64
25
40
Testprevalence Meters
above sea
level- Farm
0.33
0.23
0.32
0.23
0.47
0.58
0.28
0.17
0.24
0.80
0.31
0.07
0.27
0.07
0.40
0.36
0.45
0.20
0.40
0.07
0.33
0.04
0.42
1.00
0.51
208
313
314
340
396
434
81
215
221
265
295
299
312
376
398
418
434
436
440
484
492
748**
10
10
16
Meters
above sea
level Pasture,
lower
bounds
300
700
880
869
479
648
1100
500
700
600
757
800
800
752
600
900
648
755
400
750
1100
1000
10
10
16
Meters
above sea
levelpasture,
higher
bounds
600
930
1000
1160
900
1148
1530
750
1000
800
900
1000
977
1163
855
952
1148
1088
600
950
1520
1400
10
10
16
18
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
COAST
FJORD
FJORD
FJORD
FJORD
FJORD
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
1
1
1
1
1
53*
54
31
32
61
56*
29
60
57
24
38
45
22
27
30
23
36
37
41
26
33
42
58
49
63
28
34
55
62
35
52
46
39
59
48
43
47
51
56*
44
50
84
66
65
67
83
0.07
0.33
0.53
0.75
0.80
0.04
0.43
0.36
0.83
1.00
0.89
0.02
1.00
0.90
0.75
1.00
0.73
0.00
0.00
1.00
0.00
0.00
0.07
0.00
0.23
0.00
0.20
0.03
0.16
0.57
0.45
0.68
1.00
0.52
0.97
0.09
1.00
1.00
0.52
0.17
0.58
1.00
0.27
0.10
1.00
0.98
54
54
63
71
85
125
199
210
304
14
45
53
56
63
63
74
77
96
136
139
139
161
174
181
182
201
203
249
302
30
33
75
81
86
89
97
99
115
125
156
180
12
41
49
55
65
800
100
200
600
100
250
199
500
304
14
45
600
56
63
400
74
77
96
136
139
139
161
174
181
182
201
203
250
400
400
200
75
81
130
89
97
200
150
250
250
400
600
41
1400
480
0
1000
200
850
800
200
400
199
800
304
14
45
900
56
63
650
74
77
96
136
139
139
161
174
181
182
201
203
400
650
600
850
75
81
200
89
97
850
300
400
450
700
700
41
1400
700
600
19
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
FJORD
1
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
3
68
69
77
73
89
82
72
70
90
71
75
76
79
85
78
80
87
81
74
86
88
1.00
0.79
0.87
0.58
0.79
1.00
1.00
1.00
0.43
0.36
0.97
0.80
1.00
1.00
1.00
1.00
0.87
0.96
0.80
0.96
1.00
78
12
43
45
57
77
79
163
192
241
11
13
15
16
22
23
66
79
81
84
93
480
800
500
400
800
77
79
1150
900
241
600
500
400
16
550
23
380
550
500
84
500
700
800
700
400
1300
77
79
1300
900
241
700
900
500
16
650
23
1000
730
900
84
900
20
Table S2: Distribution of livestock farms and human populations and the level of agricultural activity represented by hours spent on agricultural
work in various municipalities in the 3 study areas during timespan 1(1980-1989), timespan 2(1990-1999) and timespan 3(2000-2008).
Location
District
Livestock farms (N)
Human population (N)
Agriculture work (hours)
Municipality Timespan1 Timespan2 Timespan3 Timespan1 Timespan2 Timespan3 Timespan1 Timespan2 Timespan3
INLAND Kviteseid
119
78
55
2975
2780
2617
279151
155469
90891
INLAND Fyresdal
70
58
43
1438
1372
1350
170157
69261
33499
INLAND Tokke
141
107
83
2757
2604
2452
278707
213712
113269
INLAND Vinje
248
191
133
3970
3970
3766
553851
333642
189001
93
72
47
1512
1365
1306
241750
132264
66014
INLAND Bygland
COAST
Sandnes
571
484
385
39631
48062
57033
1675222
1496485
926529
COAST
Lund
192
162
122
3060
3080
3111
410503
388222
220358
COAST
Bjerkreim
247
234
220
2222
2420
2481
828450
794676
625778
COAST
Hå
654
603
532
12636
13270
14589
2153668
2117462
1591263
COAST
Time
384
351
315
10879
12549
14252
1343000
1195860
915297
COAST
Gjesdal
217
197
181
6186
8031
9272
669041
586431
463301
COAST
Kvitsøy
24
21
17
527
502
522
70337
48126
23765
FJORD
Vindafjord
449
402
332
7933
8092
8133
1841696
1530267
1025189
FJORD
Etne
296
241
219
4052
3966
3910
732312
573482
399300
21
Table S3: Description of the climatic and environmental variables at infield/farm level and at rough pasture level that was significant after
univariate testing.
INFIELD/FARM LEVEL
Variable
Definition
Moose
The number of bagged moose in the municipality where the farm reside
Red Deer
The number of bagged red deer in the municipality where the farm reside
Roe Deer
The number of bagged roe deer in the municipality where the farm reside
Sheep
The number of sheep in the municipality where the farm reside
NuFarms
The number of farms in the municipality where the farm reside
F_masl
The meters above sea level at which the farm is situated
Humans
The number of inhabitants in the municipality where the farm reside
Area
Denoting district 1,2 and 3 (INLAND,COAST and FJORD)
Timespan
Denoting the 3 decades; timespan 1(80s),timespan 2(90s) and timespan 3(00s)
Nu_patch
Number of patches of bush enroachment in a 500-m radius
Meanarea_p
Mean area of patches of bush enroachment intersected by a 500-m radius (m²)
Area_shrubi
Total area covered by patches of bush enroachment intersected by a 500-m radius (m²)
SatDefMeanMay-Aug
Mean saturation deficit in May-August
RHMeanOct-Mar
Mean relative humidity October-March
SatDefMeanMay-Aug
Mean saturation deficit in May-August
TMeanJan-Dec
Annual mean air temperature
TMeanSDApr
Daily mean air temperature standard deviation in april
SnoStartDays
Number of days from 1 September to snow depth ≥2 cm
22
SnoDepth1-20Days
Number of days from 1 September with snow depth of 1-20 cm
RRSumMay
Mean monthly precipitation in May
Number of days between 1 Nov and 28 Feb where temperature decrease in GST from a day to the next
TDecr÷5<DaysNov-Feb
day are >5 °C.
TDecr÷5<DaysJan-Dec
Number of days pr year where temperature decrease in GST from a day to the next day are >5 °C.
Number of days between 1 Nov and 31 Dec where temperature increase in GST from a day to the next
TIncr÷5<DaysNov-Feb
day are >5 °C.
TIncr+5<DaysJun
Number of days in June where temperature increase in GST from a day to the next day are >5 °C.
TIncr+5<DaysNov
Number of days in November where temperature increase in GST from a day to the next day are >5 °C.
GSTminJan-Dec
Annual mean of lowest daily Ground Surface Temperature (GST); monthly basis
GSTmaxJan-Dec
Annual mean of highest daily Ground Surface Temperature (GST); monthly basis
GrowSeasDays
The length of the growing season.
PASTURE LEVEL (ROUGH GRAZING)
Variable
Definition
Nu_patch
Number of patches of bush enroachment in a 500-m radius
Meanarea_p
Mean area of patches of bush enroachment intersected by a 500-m radius (m²)
Area_shrubi
Total area covered by patches of bush enroachment intersected by a 500-m radius (m²)
TMeanJan-Dec
Annual mean air temperature
TMeanSDJan-Dec
Daily mean air temperature standard deviation; annual mean
SnoStartDays
Number of days from 1 September to snow depth ≥2 cm
SnoDepth1-20Days
Number of days from 1 September with snow depth of 1-20 cm
RRSumMar
Mean monthly precipitation in March
23
TDecr÷5<DaysJan-Dec
Number of days pr year where temperature decrease in GST from a day to the next day are >5 °C.
TIncr+5<DaysMay
Number of days in May where temperature increase in GST from a day to the next day are >5 °C.
TIncr+5<DaysOct
Number of days in Oct where temperature increase in GST from a day to the next day are >5 °C.
GSTminJan-Dec
Annual mean of lowest daily Ground Surface Temperature (GST)
GSTmaxJan-Dec
Annual mean of highest daily Ground Surface Temperature (GST)
SnoSS
Consist of SnoEndDays, SnoSum, SnoDepth≥2Days and SnoDepth>20Days
Snomean
Taking the mean of all the snowvariables
Number of days from 1 September with Black frost; daily GST < 0°C and ground bare of snow or snow
BlackFrdays
depth < 2 cm.
FTDays-SnoDepth<2 FTDaysSnoDepth≥2
Mean of FTDays-SnoDepth<2 and FTDays-SnoDepth≥2
GrowSeasDays
The length of the growing season.
24
Table S4: Descriptive statistics of the explanatory variables and their univariate relationship
with the outcome variable (presence of antibodies to A. phagocytophilum in sheep serum).
Variable name
Descriptive values
Meanarea_p
Mean
90% range
2109
0 - 7975
Area_shrubi
Pasture
BlackFrdays
RedDeer
NuFarms
TDecr÷5<DaysJanDec
RRSumMay
TMeanSDApril
TIncr+5<DaysJun
RRSumMar_
SnoStartDays
0:
1:
2:
3:
Categorized
variables*
Levels
0:
0 – 900:
900 – 2268:
2268 – 23767:
0
1
2
3
22
19
24
22
Farm
31 Farm
Mountain
56 Mountain
- 0.08 -0.92 – 1.00
-0.92
-0.92 – -0.20
-0.20 – 0.63
0.63 – 6.32
0.08 0.00 – 0.40
0.00 – 0.00
0.00 – 0.005
0.005 – 0.131
0.131 – 0.402
0.59 0.03 – 2.07
0.03 – 0.21
0.21 – 0.36
0.36 – 0.58
0.58 – 2.13
0.01 0.00 – 0.03
0.00 – 0.004
0.004 – 0.007
0.007 – 0.013
0.013 – 0.038
91
61 - 120
58 – 75
75 – 87
87 – 108
108 – 147
2.36 1.81 – 2.70
1.73 – 2.30
2.30 – 2.38
2.38 – 2.47
2.47 – 2.79
0.05 0.00 – 0.19
0.00
0.00 – 0.04
0.04 – 0.05
0.05 – 0.57
114
0 - 333
0.00
0 – 97
97 – 211
211 – 451
93.5
65.9 –
60.3 – 77.4
121.6
77.4 – 95.0
95.0 – 107.0
Prevalence (Farm
level)
Mean
50% range
0.55
0.25 – 0.91
0.45
0.19 – 0.78
0.68
0.27 – 1
0.42
0.20 – 0.46
0.47
0.09 - 0.87
0.42
0.22 – 0.45
0.63
0.31 – 0.97
0.61
0.36 – 0.94
0.59
0.14 – 1
0.51
0.26 – 0.80
0.57
0.18 – 1
0.64
0.34 – 1
0.40
0.23 – 0.53
0.59
0.32 – 0.85
0.39
0.14 – 0.52
0.45
0.17 – 0.68
0.54
0.20 – 1
0.83
0.80 – 1
0.29
0.18 – 0.39
0.76
0.75 – 1
0.48
0.02 – 0.75
0.63
0.34 – 1
0.48
0.21 – 0.79
0.76
0.52 – 1
0.45
0.14 – 0.79
0.47
0.25 – 0.62
0.40
0.02 – 0.78
0.49
0.26 – 0.76
0.49
0.24 – 0.79
0.78
0.63 – 0.99
0.59
0.34 – 0.90
0.77
0.60 – 1
0.37
0.17 – 0.51
0.39
0.02 – 0.85
0.69
0.40 – 1
0.59
0.39 - 0.82
0.59
0.31 – 0.95
0.20
0.02 – 0.28
0.57
0.18 – 1
0.34
0.20 – 0.40
0.34
0.23 – 0.45
0.72
0.57 – 0.99
0.25
0.07 – 0.35
0.53
0.24 – 0.89
0.71
0.44 – 1
25
107.0 – 124.7 0.69
0.51 – 1
88.3 85.1 – 90.9
80.3 – 86.4 0.78
0.55 – 1
RHMeanOct-Mar
86.4 – 89.2 0.51
0.09 – 0.91
89.2 – 89.8 0.44
0.17 – 0.75
89.8 – 91.6 0.38
0.21 – 0.50
0.07 0.00 – 0.23
0.00 – 0.02 0.59
0.35 – 0.95
RoeDeer
0.02 – 0.05 0.27
0.07 – 0.36
0.05 – 0.06 0.81
0.74 – 1
0.06 – 0.26 0.57
0.31 – 1
0.05 0.00 – 0.22
0.0000 – 0.0004 0.47
0.06 – 0.85
Moose
0.0004 – 0.0033 0.62
0.43 – 0.97
0.003 – 0.038 0.83
0.80 – 1
0.038 – 0.337 0.27
0.17 – 0.35
284
160 – 347
133 – 233 0.32
0.19 – 0.40
GrowSeasDays
233 – 298 0.47
0.20 – 0.80
298 – 334 0.56
0.16 – 0.94
334 – 349 0.82
0.72 – 1.00
*Continuous variables where categorized in 4 equal parts (based on quantiles)
26
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